Assignment 3

#Libraries Used:

library(tibble)
library(readr)
library(keras)

#Reading Data:

data <- read.csv("jena_climate_2009_2016.csv")
head(data)

#Preparing the data:

data <- data.matrix(data[,-1])
train_data <- data[1:200000,]
mean <- apply(train_data, 2, mean)
std <- apply(train_data, 2, sd)
data <- scale(data, center = mean, scale = std)

#Generation Function:

generator <- function(data, lookback, delay, min_index, max_index,
                      shuffle = FALSE, batch_size = 128, step = 6) {
  if (is.null(max_index))
    max_index <- nrow(data) - delay - 1
  i <- min_index + lookback
  function() {
    if (shuffle) {
      rows <- sample(c((min_index+lookback):max_index), size = batch_size)
    } else {
      if (i + batch_size >= max_index)
        i <<- min_index + lookback
      rows <- c(i:min(i+batch_size-1, max_index))
      i <<- i + length(rows)
    }
    
    samples <- array(0, dim = c(length(rows), 
                                lookback / step,
                                dim(data)[[-1]]))
    targets <- array(0, dim = c(length(rows)))
                     
    for (j in 1:length(rows)) {
      indices <- seq(rows[[j]] - lookback, rows[[j]] - 1, 
                     length.out = dim(samples)[[2]])
      samples[j,,] <- data[indices,]
      targets[[j]] <- data[rows[[j]] + delay,2]
    }            
    
    list(samples, targets)
  }
}

#Variables:

lookback <- 1440
step <- 6
delay <- 144
batch_size <- 128
train_gen <- generator(
  data,
  lookback = lookback,
  delay = delay,
  min_index = 1,
  max_index = 200000,
  shuffle = TRUE,
  step = step, 
  batch_size = batch_size
)
val_gen = generator(
  data,
  lookback = lookback,
  delay = delay,
  min_index = 200001,
  max_index = 300000,
  step = step,
  batch_size = batch_size
)
test_gen <- generator(
  data,
  lookback = lookback,
  delay = delay,
  min_index = 300001,
  max_index = NULL,
  step = step,
  batch_size = batch_size
)
# This is how many steps to draw from `val_gen`
# in order to see the whole validation set:
val_steps <- (300001 - 200001 - lookback) / batch_size
  # This is how many steps to draw from `test_gen`
# in order to see the whole test set:
test_steps <- (nrow(data) - 300001 - lookback) / batch_size

#Model 1:

model <- keras_model_sequential() %>% 
  layer_flatten(input_shape = c(lookback / step, dim(data)[-1])) %>% 
  layer_dense(units = 32, activation = "relu") %>% 
  layer_dense(units = 1)

2020-05-03 20:09:40.978208: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2

model %>% compile(
  optimizer = optimizer_rmsprop(),
  loss = "mae"
)
history <- model %>% fit_generator(
  train_gen,
  steps_per_epoch = 500,
  epochs = 30,
  validation_data = val_gen,
  validation_steps = val_steps
)

Epoch 1/30

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500/500 [==============================] - 63s 125ms/step - loss: 1.8172 - val_loss: 0.9247
Epoch 2/30

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Epoch 3/30

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Epoch 4/30

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Epoch 5/30

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Epoch 7/30

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Epoch 8/30

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Epoch 9/30

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Epoch 10/30

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Epoch 11/30

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Epoch 12/30

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Epoch 13/30

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Epoch 14/30

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Epoch 15/30

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Epoch 17/30

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Epoch 18/30

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500/500 [==============================] - 61s 122ms/step - loss: 0.2016 - val_loss: 0.3287
Epoch 19/30

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500/500 [==============================] - 63s 126ms/step - loss: 0.1989 - val_loss: 0.3536
Epoch 20/30

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#Model 2 with LSTM layer